Consumptive Behavior and Saving Ring Workers Case Study in Cihideunghilir Kuningan, Indonesia

Income is the response from what we do such as salary, rent interest, commission, expanse, and profit. The ring craftsman gets income from making a ring, and that income will use to consuming thing or merits that they need. The high income can increase consumption ability and make the consumption higher. The high income can increase saving ability. Problem formulation in this research is first is the income influential to the consumption pattern of ring craftsman at Cihideunghilir Village Cidahu Subdistrict Kuningan Regency. Two is the income influential to saving ability of ring craftsman at Cihideunghilir Village Cidahu Kuningan West Java

Factors Affecting Job Satisfaction among Academic Employees in Polytechnic

Satisfied employees are crucial in developing a successful organization. The purpose of this study is to identify the factors that affect job satisfaction among polytechnic academic employees in Malaysia. A cross-sectional study was employed and stratified random sampling was used to collect the data. A total of 130 respondents answered the questionnaires. The results revealed that job security, salary and working conditions had significant and positive influence on job satisfaction among polytechnic academic employees. Hence, strengthening the factors of job security, salary and working conditions among the polytechnic academic employees is critically important to ensure the employees are satisfied which will then lead towards a successful polytechnic

Physiological-based Driver Monitoring Systems: A Scoping Review

A physiological-based driver monitoring system (DMS) has attracted research interest and has great potential for providing more accurate and reliable monitoring of the driver’s state during a driving experience. Many driving monitoring systems are driver behavior-based or vehicle-based. When these non-physiological based DMS are coupled with physiological-based data analysis from electroencephalography (EEG), electrooculography (EOG), electrocardiography (ECG), and electromyography (EMG), the physical and emotional state of the driver may also be assessed. Drivers’ wellness can also be monitored, and hence, traffic collisions can be avoided. This paper highlights work that has been published in the past five years related to physiological-based DMS. Specifically, we focused on the physiological indicators applied in DMS design and development. Work utilizing key physiological indicators related to driver identification, driver alertness, driver drowsiness, driver fatigue, and drunk driver is identified and described based on the PRISMA Extension for Scoping Reviews (PRISMA-Sc) Framework. The relationship between selected papers is visualized using keyword co-occurrence. Findings were presented using a narrative review approach based on classifications of DMS. Finally, the challenges of physiological-based DMS are highlighted in the conclusion. Doi: 10.28991/CEJ-2022-08-12-020 Full Text: PD

Piloting for interviews in qualitative research: operationalization and lessons learnt

Qualitative interviews offer rich and detailed information in understanding people’s experiences. However, qualitative inquiry might be difficult for inexperience researcher to adequately perform the interview. Piloting for interview is an integral aspect and useful in the process of conducting qualitative research as it highlights the improvisation to the major study. This article discusses the importance of pilot study, the methods undertaken and the lessons learnt throughout the process. The pilot interview was conducted with two offshore catering employees, as preparation for a dissertation in developing a job satisfaction instrument for offshore catering employees in Malaysia. The useful functions of pilot study are described and in highlighting the advantageous of pilot study, this paper describes the modification made for the major study as a result of the pilot work. These comprise (1) criteria for selecting potential participants, and (2) improving the interview guide, particularly the interview questions

Phylogeny and morphometric variation of several weevils species (Coleoptera: Curculionidae) from Malaysia

The phylogeny of 43 species under 20 genera of seven subfamilies of weevils’ species (Coleoptera: Curculionidae) from Malaysia were successfully inferred based on 34 morphological characters, of which combined the qualitative and quantitative inputs. The monophyletic clade of the Curculionidae was supported by Neighbor Joining (NJ) and Maximum Parsimony (MP) trees with bootstraps values of 79% and 76%, respectively. Although the arrangement and placement among the 19 species of Dryophthorinae were not fully resolved, however the phylogenies were able to elucidate the relationships of the other curculionids from the Curculioninae, Entiminae, Lixinae and Molytinae subfamilies. Therefore, further multivariate analyses were conducted on 17 selected species of Curculionidae, which has been successful to discriminate the examined species. From the principal component analysis (PCA; eigenvalues of PC1 for cluster 1 = 7.4650; eigenvalues of PC1 for cluster 2 = 5.1874) and canonical variate analysis (CVA; cluster 1 with p < 0.0001; cluster 2 with p < 0.0001), the diagnostic morphological characters were resulted from the elytron, pronotum, total length of body, and femur length. As a conclusion, the morphometrics has proven to be reliable and informative as another alternative to subfamilies classification and to show the relationships within the examined insect’s species. However, it is also recommended that further studies should include more diagnostic and informative characters to represent up to the tribes or genus levels in future

Attention Based Spatial-Temporal GCN with Kalman filter for Traffic Flow Prediction

Intelligent Transportation Systems (ITS) are becoming increasingly important as traditional traffic management systems struggle to handle the rapid growth of vehicles on the road. Accurate traffic prediction is a critical component of ITS, as it can help improve traffic management, avoid congested roads, and allocate resources more efficiently for connected vehicles. However, modeling traffic in a large and interconnected road network is challenging because of its complex spatio-temporal data. While classical statistics and machine learning methods have been used for traffic prediction, they have limited ability to handle complex traffic data, leading to unsatisfactory accuracy. In recent years, deep learning methods, such as Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs), have shown superior capabilities for traffic prediction. However, most CNN-based models are built for Euclidean grid-structured data, while traffic road network data are irregular and better formatted as graph-structured data. Graph Convolutional Neural Networks (GCNs) have emerged to extend convolution operations to more general graph-structured data. This paper reviews recent developments in traffic prediction using deep learning, focusing on GCNs as a promising technique for handling irregular, graph-structured traffic data. We also propose a novel GCN-based method that leverages attention mechanisms to capture both local and long-range dependencies in traffic data with Kalman Filter, and we demonstrate its effectiveness through experiments on real-world datasets where the model achieved around 5% higher accuracy compared to the original model

Decoding the future: genomic sequencing's vital role in communicable disease prevention within public health practice - a scoping review

Precision public health using genomic sequencing is a new field that has been gained interest by public health practitioner for prevention and control measures. The objective of this study is to identify the various applications of genomic sequencing for prevention of communicable disease in public health practice. Articles containing relevant keywords were identified using a systematic search strategy applied in Medline, Scopus, and Springer electronic databases. Full text of included in the study were retrieved and categorized. A total of 24 articles were included in the final review. The main themes regarding application of genomic sequencing in the prevention of communicable disease that was found in the articles were describing transmission patterns, investigating outbreaks, diagnosing infection, developing and evaluating interventions including vaccines, outcomes response treatment and monitoring antimicrobial resistance. In conclusion, genomic sequencing has the potential to enhance the prevention and control of communicable diseases globally

Prevalence and distributions of severely elevated low-density lipoprotein cholesterol (LDL-c) according to age, gender and clinic location among patients in the Malaysian primary care

BackgroundAdults with severely elevated low-density lipoprotein cholesterol (LDL-c) may have familial hypercholesterolaemia (FH) and are at high risk of atherosclerotic cardiovascular disease (ASCVD). The prevalence of elevated LDL-c in primary care clinics in Malaysia is not known. Therefore, this study aimed to determine the prevalence and distributions of severely elevated LDL-c among adult patients attending public primary care clinics in Malaysia.MethodsA cross-sectional study was conducted at 11 public primary care clinics in the central states of Malaysia, among adults ≥18 years old with LDL-c recorded in the electronic medical record. Sociodemographic and LDL-c data from 2018 to 2020 were extracted. Severely elevated LDL-c was defined as ≥4 mmol/L, which were further classified into: 4.0–4.9, 5.0–5.9, 6.0–6.9 and ≥ 7 mmol/L.ResultsOut of 139,702 patients, 44,374 (31.8 %) had severely elevated LDL-c of ≥4 mmol/L of which the majority were females (56.7 %). The mean (±SD) age of patients with severely elevated LDL-c was younger at 56.3 (±13.2) years compared to those with LDL-c of <4.0 mmol/L at 59.3 (±14.5) years. In terms of LDL-c levels, 30,751 (69.3 %), 10,412 (23.5 %), 2,499 (5.6 %) and 712 (1.6 %) were in the 4.0–4.9, 5.0–5.9, 6.0–6.9 and ≥ 7 mmol/L categories, respectively.ConclusionThe prevalence of severely elevated LDL-c of ≥4.0 mmol/L among adult patients in public primary care clinics was high. These patients need to be further investigated for secondary and inherited causes such as FH. Therapeutic lifestyle modification and pharmacological management are pivotal to prevent ASCVD in these patients

Optimal power dispatch of hybrid PV/diesel systems using heuristic bio-inspired algorithms

Generally due to the strategic equatorial region, Malaysia is advancing into solar energy as a replacement of alternative source for an electrical energy production to meet the escalation load demand. Thus, the integrated hybrid system like PVGenset set system are developed to generate power to meet load demand where it can be fitted into its local geography and others according to specification. However, this does not guarantee the hybrid system to generate power optimally as weather conditions (solar insolation, temperature and others) changes periodically and influence the power generation and the power dispatch to the load. Therefore, the hybrid system does not operate at the optimal state and without a proper dispatching controller it may lead to over stress one or the other hybrid system component causes frequent wear and tear with higher maintenance cost to the system. In order to curb this situation, the hybrid system requires a specific approach along with a controller to search and to dispatch the hybrid PV-Genset system generated power at the best potential optimal state. A Bio-Heuristic approach can be applied to determine the optimal power generation while a dispatch controller dispatches the electric hybrid power system to the load demand. The aim of this research is to implement the selected bio-heuristic approach such as Particle Swarm Optimisation (PSO) while Fuzzy Logic is used as a dispatch controller for a small scale hybrid PV-Genset system. The simulation of the hybrid PV-Gertset system modelling is simulated using two types of tropical weather conditions (sunny and rainy). From this research, simulation results are obtained and series of analysis is conducted using MATLAB/SlMULINK. Through the analysis, results have shown the contribution of each hybrid system component operates at the optimum level while power is dispatch to the load based on the hybrid system capability

Route Planning Analysis In Holes Drilling Process Using Magnetic Optimization Algorithm For Electronic Manufacturing Sector

Electronic manufacturing sector uses computer numerical controlled machines for drilling holes. Most of the computer numerical controlled machines used nearest neighbour algorithm to plan the route for the drill bit to travel. Based on this motivation, this paper proposes an approach which is based on the experimentation of Magnetic Optimization Algorithm. In this implementation, each magnetic agent or particle in Magnetic Optimization Algorithm represents a candidate solution of the problem. The magnitude of the magnetic force between these particles is inversely proportional to the distance calculated by the solution they represented. Particles with greater magnetic force will attract other particles with relatively smaller magnetic force, towards it. The process is repeated until the stopping condition meets and the solution with lowest distance is taken as the best-found solution. Result obtained from the case study shows that the proposed approach managed to find the optimal solution. With this method, electronics manufacturing sector can optimize the drilling process hence will increase the productivity of the manufacturer. This study can be extended further by tuning the parameters of MOA in order to enhance the drilling route process